Skip to main content

THREE.JS : display image on objects issue

I am currently creating a webpage in three.js and I struggle to display an image on an object. What I want to do is to display an image on a Mesh with PlaneGeometry.

I first tried to load my image as a texture to replace the material of my mesh but it failed it doesn't display anything (even the object disappeared).

To create and display my object I used these lines of code (scene is my scene and onglets is the group in which I gathered several objects (onglet1, onglet2, ...)):

    couleur = new THREE.MeshBasicMaterial( {color: 0x031f3c , side: THREE.DoubleSide } );
    plan = new THREE.PlaneGeometry( 0.75 , 0.4 );
    var onglets = new THREE.Group();

    onglet1 = new THREE.Mesh( plan , couleur );
    onglet1.position.set( 0, 0, r );
    onglets.add(onglet1);
    scene.add(onglets);

To load my image I modified my code like this:

    couleur = new THREE.MeshBasicMaterial( {color: 0x031f3c , side: THREE.DoubleSide } );
    plan = new THREE.PlaneGeometry( 0.75 , 0.4 );
    var onglets = new THREE.Group();

    var map = new THREE.TextureLoader().load( "../media/groupe.jpg" );
    var material = new THREE.SpriteMaterial( { map: map, color: 0x000000 } );

    onglet1 = new THREE.Mesh( plan , material );
    onglet1.position.set( 0, 0, r );
    onglets.add(onglet1);
    scene.add(onglets);

If you see what I did wrong or have any advice to improve my code in general I would be happy to hear it. Thanks in advance for the help guys!

Via Active questions tagged javascript - Stack Overflow https://ift.tt/2FdjaAW

Comments

Popular posts from this blog

How to show number of registered users in Laravel based on usertype?

i'm trying to display data from the database in the admin dashboard i used this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count(); echo $users; ?> and i have successfully get the correct data from the database but what if i want to display a specific data for example in this user table there is "usertype" that specify if the user is normal user or admin i want to user the same code above but to display a specific usertype i tried this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count()->WHERE usertype =admin; echo $users; ?> but it didn't work, what am i doing wrong? source https://stackoverflow.com/questions/68199726/how-to-show-number-of-registered-users-in-laravel-based-on-usertype

Why is my reports service not connecting?

I am trying to pull some data from a Postgres database using Node.js and node-postures but I can't figure out why my service isn't connecting. my routes/index.js file: const express = require('express'); const router = express.Router(); const ordersCountController = require('../controllers/ordersCountController'); const ordersController = require('../controllers/ordersController'); const weeklyReportsController = require('../controllers/weeklyReportsController'); router.get('/orders_count', ordersCountController); router.get('/orders', ordersController); router.get('/weekly_reports', weeklyReportsController); module.exports = router; My controllers/weeklyReportsController.js file: const weeklyReportsService = require('../services/weeklyReportsService'); const weeklyReportsController = async (req, res) => { try { const data = await weeklyReportsService; res.json({data}) console...

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...